干旱条件下春小麦各基因型性状遗传多样性的主成分聚类分析

M. Siddquie, M. Hoque
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引用次数: 0

摘要

遗传多样性在种质改良中起着重要作用,直接关系到作物的生产力。许多统计方法被用来研究不同作物基因型间的遗传多样性。像主成分分析和聚类分析这样的方法是有用的,并且最常用于单独鉴定植物性状和帮助育种者遗传增强小麦基因型的属性。本研究在位于孟加拉国拉杰沙希Shyampur的孟加拉国农业研究所(BARI)农场研究部(OFRD)试验田,采用主成分分析和聚类分析方法,研究干旱条件下高产小麦基因型及其重要农艺和生理性状的遗传多样性和选择。采用7 × 10 α晶格设计,对2018-2019年非灌溉干旱条件下的70个面包小麦基因型进行了评价。特征值大于1.0的前4个主成分(PCs)占总变异数的82.81%。PC1的最大性状为穗数m-2 (SPM)、千粒重(TGW)、土地盖度(GC)、归一化植被指数(NDVI)、产量(GY)、生物量(BM)和收获指数(HI), PC2的最大性状为抽穗日数(HD)和收获指数(BM)。对PC3性状贡献最大的是籽粒穗1 (GPS)和GC,而对PC4性状贡献最大的是叶片相对含水量(RWC)。主成分双图选择了21个比平均产量高的基因型,因为它们分布在PC1的正侧。聚类分析将70个基因型分为6个不同的聚类。主成分双图选择的21个基因型Ⅱ聚类具有最高的SPM(257.4)、GPS(42.2)、TGW (40.51 g)、GC(0.27)、NDVI(0.73)、SPAD(44.24)、RWC(88.33%)、籽粒产量(3216kg ha-1)、BM (8535 Kg ha-1)和HI(0.37),属于营养期冠层温度最低(16.14˚C)和灌浆期冠层温度最低(24.64˚C)和中度HD (71.65 d)。根据目前的研究结果,最佳基因型可作为未来抗旱育种计划的重要育种材料。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic diversity based on Principal Component and cluster analysis for various characters in spring wheat genotypes under drought condition
Genetic diversity plays an important function in the improvement of germplasm which has a direct association with the crop productivity. A number of statistical methods have been employed to investigate genetic diversity among the genotypes of various crops. Approaches like principal component and cluster analysis are useful and most frequently used for identifying plant characters individually and assisting breeders in genetically enhancing attributes in wheat genotypes. This research was carried out at the experimental field of On-farm Research Division (OFRD), Bangladesh Agricultural Research Institute (BARI), Shyampur, Rajshahi, Bangladesh, to study the genetic diversity and selection of high yielding wheat genotypes with their important agronomic and physiological traits among studied genotypes in drought condition by using principal component and cluster analyses. A total of 70 bread wheat genotypes were evaluated in 7 × 10 alpha lattice design in non-irrigated drought conditions during 2018-2019 cropping season. The first four principal components (PCs) with eigen values greater than 1.0 accounting for 82.81% of the total observed variation among genotypes. Traits with maximum values in PC1 were spikes m-2 (SPM), thousand grain weight (TGW), ground coverage (GC), normalized difference vegetation index (NDVI), grain yield (GY), biomass (BM), and harvest index (HI) while PC2 comprised heading days (HD) and BM. The major contributors to PC3 were grains spike-1 (GPS) and GC, whereas the maximum value of trait in PC4 was in relative leaf water content (RWC). The principal component biplot selected 21 high yielding genotypes than the average yield as they were distributed on the positive side of the PC1. The cluster analysis grouped 70 genotypes into six diverse clusters. Cluster Ⅱ containing same 21 genotypes previously selected by principal component biplot provided the highest SPM (257.4), GPS (42.2), TGW (40.51 g), GC (0.27), NDVI(0.73), SPAD (44.24), RWC (88.33%), grain yield (3216kg ha-1), BM (8535 Kg ha-1) and HI (0.37) belonging to the lowest canopy temperature at vegetative stage (16.14˚C) and canopy temperature at grain filling stage (24.64˚C) and moderate HD (71.65 days). Based on the results of the current study the best genotypes can be used as important breeding materials in upcoming breeding schemes for drought tolerance.
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